We further evaluate the performance of our proposed unrolling scheme by employing an alternative learning module, i.e., selecting a different function \(\Psi \)  in (REF ).
Our goal is to characterize how the achieved performance depends on the specific selection of the GNN.
To that end, we compare our canonical choice (REF )-() with an REGNN [1]}, primarily due to its simple and lightweight architecture.
REGNN also interprets the CSI as the adjacency matrix of a graph and constructs a layered architecture by intertwining linear graph filters with point-wise non-linearities.
To be more precise, if we denote by \({\mathbf {z}}_l\)  the output of an intermediate layer in an REGNN, we have that
\({\mathbf {z}}_l = \alpha \left( \sum _{k=0}^K \nu _{lk} {\mathbf {H}}^k {\mathbf {z}}_{l-1}\right),\) 
